tutorial.md

layout	title	zenodo_link	requirements	questions	objectives	time_estimation	key_points	contributors
tutorial_hands_on	Functionally Assembled Terrestrial Ecosystem Simulator (FATES)	https://doi.org/10.5281/zenodo.4108341	type topic_name tutorials internal climate panoply type topic_name tutorials internal galaxy-interface history-to-workflow	How to run CLM-FATES with the CLM-FATES Galaxy tool? How to upload input data for running CLM-FATES? How to customize your runs? How to analyze your model outputs? How to create a workflow? How to share your workflow?	Setting up a CLM-FATES case. Customizing your run. Interactive visualization with Panoply. Automating your analyzes and visualisations of your CLM-FATES case. Creating multi-case scenarios. Composing, executing and publishing CML-FATES workflow.	4H	CLM-FATES is a numerical terrestrial ecosystem model used in climate models Panoply is a quick visualization tools for plotting your results Multi-case simulations can be easily developed and shared with a Galaxy workflow	annefou huitang-earth

Introduction

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Terrestrial ecosystem models have been widely used to study the impact of climate changes on vegetation and terrestrial biogeochemical cycles in climate modelling community. They are also more and more applied in ecological studies to help ecologists to better understand the processes. But the technical challenges are still too high for most of the ecologists to use them. This practical aims at familiarizing you (especially ecologists) with running a terrestrial ecosystem model (i.e., CLM-FATES) at site-level in Galaxy and analyzing the model results. It will also teach you on how to create Galaxy workflow for your site-level CLM-FATES simulations to make your research fully reproducible. We hope this tutorial will promote the use of CLM-FATES and other terrestrial ecosystem models by a broader community.

Agenda

In this tutorial, we will cover:

1. TOC {:toc}

{: .agenda}

{% icon comment %} Background

FATES is the “Functionally Assembled Terrestrial Ecosystem Simulator”, which is a vegetation demographic model ({% cite Fisher2017 %}). FATES needs what we call a "Host Land Model" (HLM) to run and in this tutorial we will be using the Community Land Model of the Community Terrestrial Systems Model (CLM-CTSM). FATES was derived from the CLM Ecosystem Demography model (CLM(ED)), which was documented in {% cite Fisher2015 %} and {% cite Koven2020 %}. And this technical note was first published as an appendix to that paper. The FATES documentation will provide some more insight on FATES too.

{: .comment}

Get CLM-FATES input data

Preparing CLM-FATES input data is out of scope for this tutorial. We assume the input data tarball contains the following folders:

atm   cpl   lnd   share

Each sub-folder will then contain all the necessary inputs for running your CLM-FATES case. For instance, 'atm' contains all the meteorological forcing data for running CLM-FATES. 'lnd' contains the data required to describe surface conditions (e.g., soil depth) for the model. More details about the model inputdata can be found in CLM and FATES documentation. For the purpose of this tutorial, input data for a single point location (ALP1) on the Norwegian alpine tundra ecosystem (Latitude: 61.0243N, Longitude: 8.12343E, Elevation: 1208 m) has been prepared and is ready to use. This is a site included in the modelling platform developed under [EMERALD project] (https://www.mn.uio.no/geo/english/research/projects/emerald/). More details about the sites can be found in {% cite Klanderud2015 %} and {% cite Vigdis2020 %}

{% icon hands_on %} Hands-on: Data upload

1. Create a new history for this tutorial. If you are not inspired, you can name it fates.

   {% snippet faqs/galaxy/histories_create_new.md %}

2. Import the input data and the restart dataset from Zenodo or from the shared data library. Restart dataset will be used if you want to initialize the model from exisiting experiments rather than running the model from a cold start to shorten spin-up time needed for the model.

   https://zenodo.org/record/4108341/files/inputdata_version2.0.0_ALP1.tar
   https://zenodo.org/record/4126404/files/CTSM_FATES-EMERALD_version2.0.0_ALP1_restart_2300-01-01.tar
   

   {% snippet faqs/galaxy/datasets_import_via_link.md %}

   {% snippet faqs/galaxy/datasets_import_from_data_library.md %}

3. Check the datatype (for both files) is tar

   {% snippet faqs/galaxy/datasets_change_datatype.md datatype="datatypes" %}

4. Rename {% icon galaxy-pencil %} datasets

   • Dataset names are the full URL, but this is not very nice to work with, and can even give errors for some tools
   • It is good practice to change the dataset names to something more meaningful and without any special characters
     • E.g. by stripping off the beginning of the URL
   • Example: rename https://zenodo.org/record/4108341/files/inputdata_version2.0.0_ALP1.tar to inputdata_version2.0.0_ALP1.tar
   • Do the same for the other dataset

   {% snippet faqs/galaxy/datasets_rename.md %}

{: .hands_on}

Setting up a CLM-FATES simulation

We will be using the CTSM/FATES-EMERALD Galaxy tool.This tool is based on the version of CLM-FATES that have been adapted to run at the sites included in the EMERALD project. More details about this model version can be found in README_fates_emerald_api

{% icon comment %} Tip: Finding your tool

Different Galaxy servers may have tools available under different sections, therefore it is often useful to use the search bar at the top of the tool panel to find your tool.

Additionally different servers may have multiple, similarly named tools which accomplish similar functions. When following tutorials, you should use precisely the tools that they describe. For real analyses, however, you will need to search among the various options to find the one that works for you.

{: .comment}

{% icon comment %} Tip: Pre-selected tool parameters

When selecting a tool, Galaxy will pre-fill the tool parameters, selecting the first dataset with the corresponding type in your history. Be aware that very often, the default pre-selection is incorrect and do not correspond to the required dataset. So always check and update accordingly the tool parameters!

{: .comment}

{% icon hands_on %} Hands-on: Creating a new CTSM/FATES-EMERALD case

1. {% tool CTSM/FATES-EMERALD %} with the following parameters:

   • {% icon param-file %} "inputdata for running FATES EMERALD": inputdata_version2.0.0_ALP1.tar file from your history
   • "Name of your case": ALP1_exp
   • In section "Customize the model run period":
     • {% icon param-select %} "Determines the model run initialization type:: hybrid
       • "Reference case for hybrid or branch runs": ALP1_refcase
       • "Reference date for hybrid or branch runs (yyyy-mm-dd)": 2300-01-01
       • "Run start date (yyyy-mm-dd). Only used for startup or hybrid runs": 0001-01-01
       • {% icon param-file %} "Restart for running FATES EMERALD": CTSM_FATES-EMERALD_version2.0.0_ALP1_restart_2300-01-01.tar
     • "Provides a numerical count for STOP_OPTION": 5
     • "Sets the run length along with STOP_N and STOP_DATE": nyears

   {% icon comment %} Startup versus Hybrid

   When using startup, the FATES model will start from some arbitrary baseline state that is not linked to any previous run. Startup runs are typically initialized using a start date of 0001-01-01 except if you change it (start date option). For any scientific study, starting from an arbitraty baseline state implies you would need to run the model for a long period (between 100 and 200 years) before being able to use the model outputs. For this reason, we usually make a first simulation (spin-up) in startup mode and reuse this case as a baseline for our scientific study. We then use hybrid type and give additional inputs (restart files) to our simulation case. It is then important to specify the dates of your restart files. This is what we do in this tutorial.

   {: .comment}

2. Check that the datatype {% icon galaxy-pencil %} of your outputs (history file) is netcdf

   • If this is not the case, please change the datatype now

   {% icon comment %} About CLM-FATES history files

   All the CLM-FATES history files are organized in a collection.

   {: .comment}

   {% icon comment %} About datatypes

   All the history files contain gridded data values written at specified times during the model run. Depending on the length of your simulation, you may have one or more history files that you can recognize from their names: ALP1_exp.clm2.h0.yyyy-mm-dd-sssss.nc (for non-monthly history files). Datatypes are, by default, automatically guessed. Here, as the prefix is .nc, the format is not always recognized as netcdf files. To cope with that, one can change the datatype manually, as shown below. {: .comment}

   {% snippet faqs/galaxy/datasets_change_datatype.md datatype="datatypes" %}

3. Rename {% icon galaxy-pencil %} the output dataset (history file) to ALP1_exp.nc

   Our FATES model has run for 5 years only, so we get a single output file. As previously, we recommend to rename all netCDF files so that they do not contain any special characters or dots (except for the file extension) or slashes. Some tools, in particular Panoply, won't be able to recognize your file if not named properly.

   {% snippet faqs/galaxy/datasets_rename.md %}

4. {% tool NetCDF xarray Metadata Info %} to get metadata information for CLM-FATES netCDF outputs:

   • {% icon param-file %} "Netcdf file": ALP1_exp.nc

5. Inspect {% icon galaxy-eye %} the generated output files

   • Identify which variables would provide you some insights about canopy transpiration.

   {% icon question %} Questions

   1. What are the short names of the relevant variables? Which one will you pick if you want a result in mm/s?
   2. What are the dimensions of these variables?

   {% icon solution %} Solution

   1. FCTR is the canopy transpiration in W/m^2 and QVEGT is in mm/s. Therefore, we would select the latter.
   2. These variables are stored as a function of time and lndgrid and since we have only one grid cell, lngrid=1, hence the time series. {: .solution} {: .question}

{: .hands_on}

Quick visualization with Panoply

Opening up Panoply

{% icon hands_on %} Hands-on: Launch Panoply

Panoply plots geo-referenced and other arrays from netCDF and is available as a Galaxy interactive environment and may not be available on all Galaxy servers.

{% icon tip %} Tip: Launch Panoply in Galaxy

Currently Panoply in Galaxy is available on useGalaxy.eu instance, on the "Interactive tools" tool panel section or, as all interactive tools, from the dedicated useGalaxy.eu subdomain: Live.useGalaxy.eu. You may have to login again to Live.usrGalaxy.eu (use the same username and password than on other useGalaxy.eu subdomains) and switch to the correct history.

1. Open the Panoply tool {% icon tool %} by clicking here{:target="_blank"}
2. Check ALP1_exp.nc dataset selected in the netcdf input field
3. Click Execute
4. The tool will start running and will stay running permanently
5. Click on the "User" menu at the top and go to "Active Interactive Tools" and locate the Panoply instance you started.
6. Click on your Panoply instance
7. Click on ALP1_exp.nc dataset {: .tip} {: .hands_on}

Inspect metadata

{% icon hands_on %} Hands-on: Inspect dataset

1. Inspect dataset content

   Here you can look at the dataset (ALP1_exp.nc) and related variables (FSDS, FSA, AREA_TREE, BIOMASS_CANOPY, etc.)

   {% icon question %} Question

   1. What is the long name of MORTALITY?
   2. What is its physical unit?

   {% icon solution %} Solution

   1. Rate of total mortality per PFT (Plat functional types)
   2. indiv/ha/yr {: .solution} {: .question}

2. Plot the total carbon in live plant leaves (LEAFC)

   Cutomize your plot and save it as png file in the output folder. Remember that if you do not save in the output folder, your plot will get lost.

   {% icon question %} Question

   1. Can you observe any pattern? Does it make any sense?

   {% icon solution %} Solution

   1. We can clearly see a seasonal cycle. {: .solution} {: .question}

3. Plot the rate of total mortality per PFT (MORTALITY)

   Select a 2D plot with time as x-axis and colored by the rate of total mortality per PFT (Plant functional type). Make sure to adjust the y-axis and save your plots in the output folder (as png file).

   {% icon question %} Question

   1. Can you observe any pattern? Does it make any sense?

   {% icon solution %} Solution

   1. We can clearly see a seasonal cycle of PFT2. {: .solution} {: .question}

   {% icon comment %} Quit Panoply properly to save your plots!

   To make sure all your plots stored in outputs folder get exported to Galaxy, you need to quit panoply: File --> Quit Panoply. {: .comment} {: .hands_on}

Using Galaxy tools for analysing your CLM-FATES simulation

Panoply is a great tool for exploring the results of your simulations but what we would like is to automate the generation of the plots so that we can reuse it for any simulations.

{% icon hands_on %} Hands-on: Select and plot LEAFC

1. {% tool NetCDF xarray Selection %} to select the total carbon in live plant leaves (LEAFC)

   • {% icon param-file %} "Input netcdf file": ALP1_exp.nc
   • {% icon param-file %} "Tabular of variables": Metadata info from ALP1_exp.nc (output of NetCDF xarray Metadata Info {% icon tool %})
   • {% icon param-select %} "Choose the variable to extract": LEAFC

2. Rename {% icon galaxy-pencil %} dataset to NetCDF xarray Selection on ALP1_exp.nc

   {% snippet faqs/galaxy/datasets_rename.md %}

3. {% tool Replace parts of text %} to clean date column for plotting:

   • {% icon param-file %} "File to process": NetCDF xarray Selection on ALP1_exp.nc
   • {% icon param-text %} "Find pattern": 00:00:00
   • "Find-Pattern is a regular expression": No
   • "Replace all occurences of the pattern": Yes
   • "Case-Insensitive search": No
   • "Find whole-words": Yes
   • "Ignore first line": Yes
   • {% icon param-select %} "Find and Replace text in": entire line

4. Rename {% icon galaxy-pencil %} dataset to LEAFC_clean.tabular

   {% snippet faqs/galaxy/datasets_rename.md %}

5. Scatterplot w ggplot2 {% icon tool %} to plot the total carbon in live plant leaves (LEAFC):

   • {% icon param-file %} "Input in tabular format": LEAFC_clean.tabular
   • "Column to plot on x-axis": 1
   • "Column to plot on y-axis": 4
   • "Plot title": Total carbon in live plant leaves
   • "Label for x axis": Time
   • "Label for y axis": LEAFC (kgC ha-1)
   • In Advanced Options
     • {% icon param-select %} "Type of plot": Points and Lines
   • In Output options
     • "width of output":19.0
     • "height of output": 5.0

6. View {% icon galaxy-eye%} the resulting plot:

   

{: .hands_on}

Convert your analysis history into a Galaxy workflow

{% icon hands_on %} Hands-on: Extract workflow

1. Go to the History Options menu {% icon galaxy-gear %} menu

   • Select the Extract Workflow option.
   • Remove any unwanted steps, in particular all steps with Panoply as we do not want to have interactive tools in our automated workflow..

2. Rename the workflow to something descriptive

   • For example: CLM-FATES_ ALP1 simulation (5 years).
   • If there are any steps that shouldn't be included in the workflow, you can uncheck them.

3. Click "Create Workflow"

   • Click on "edit" and check your workflow
   • Check all the steps

{: .hands_on}

Change your CLM-FATES case and rerun your workflow

We would like to run a CLM-FATES case where the atmospheric Carbon Dioxyde Concentration (CO2) is increased by a factor of 4.

{% icon hands_on %} Hands-on: Compare the two simulations

Using the results from your two CLM-FATES simulations and the generated plots, assess the impact of an increase in the atmosperhic CO2 on the outputs of the model.

1. Open the workflow editor

   {% snippet faqs/galaxy/workflows_edit.md %}

2. Edit your workflow and customize it to run your new CO2 experiment. For this you would need to:

   • In "Advanced customization", change "Atmospheric CO2 molar ratio (by volume) only used when co2_type==constant (umol/mol)" from 367.0 to 1468.0.
   • Add an extra step to extract the first history file from the history collection: {% tool Extract Dataset %} and make sure to select "netcdf" in the change datatype field.
   • Generate the corresponding plot. The final workflow would be similar to the one shown below:

   

   {% icon question %} Question

   1. Is the model response to this significant increase of atmospheric CO2 what you expected? Justify your answer.
   2. Is the current workflow (in particular the variables selected for the plots) the best choice? What changes/additions would you recommend?

   {% icon solution %} Solution

   1. Running 5 years is already sufficient to highlight significant changes.
   2. Many suggestions can be given here. One simple addition can be the generation of plots where both simulations are represented on the same plot. {: .solution} {: .question}

{: .hands_on}

Share your work

One of the most important features of Galaxy comes at the end of an analysis. When you have published striking findings, it is important that other researchers are able to reproduce your in-silico experiment. Galaxy enables users to easily share their workflows and histories with others.

To share a history, click on the {% icon galaxy-gear %} icon in the history panel and select Share or Publish. On this page you can do 3 things:

1. Make History Accessible via Link. This generates a link that you can give out to others. Anybody with this link will be able to view your history.
2. Make History Accessible and Publish. This will not only create a link, but will also publish your history. This means your history will be listed under Shared Data → Histories in the top menu.
3. Share with a user. This will share the history only with specific users on the Galaxy instance.

{% icon comment %} Permissions

Different servers have different default permission settings. Some servers create all of your datasets completely private to you, while others make them accessible if you know the secret ID.

Be sure to select Also make all objects within the History accessible whenever you make a history accessible via link, otherwise whomever you send your link to might not be able to see your history. {: .comment}

{% icon hands_on %} Hands-on: Share history

1. Share your history with your neighbour (ask for his/her galaxy username).
2. Find the history shared by your neighbour. Histories shared with specific users can be accessed by those users under their top masthead "User" menu under Histories shared with me. {: .hands_on}

{% icon comment %} Publish your history to https://workflowhub.eu/

One step further is to share your workflow on https://workflowhub.eu where it will be stored in a Galaxy workflow format as well as in Common Workflow Language. It provides standardised workflow identifiers and descriptions needed for workflow discovery, reuse, preservation, interoperability and monitoring and metadata harvesting using standard protocols. Please note that https://workflowhub.eu is still under active development. {: .comment}

Conclusion

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We have learnt to run single-point simulations with FATES-CLM and generate workflows for multi-site scenarios.